Abstract-A nonparametric clustering technique incorporating the concept of similarity based on the sharing of near neighbors is pre-sented. In addition to being an essentially paraliel approach, the com-putational elegance of the method is such that the scheme is applicable to a wide class of practical problems involving large sample size and high dimensionality. No attempt is made to show how a priori problem knowledge can be introduced into the procedure. Index Terms-Clustering, nonparametric, pattern recognition, shared near neighbors, similarity measure. I
In this paper, a novel similarity measure for estimating the degree of similarity between two symbol...
In this paper, we introduce a novel similarity measure for relational data. It is the first measure ...
Similarity-based clustering is a simple but powerful technique which usually results in a clustering...
Clustering is an unsupervised learning technique which aims at grouping a set of objects into cluste...
In many algorithms in the field of data mining to perform clustering of given data, notion of ‘clust...
Pairwise clustering methods partition the data space into clusters by the pairwise similarity betwee...
Abstract The task of clustering is to identify classes of similar objects among a set of objects. Th...
A nonparametric, hierarchical, disaggregative clustering algorithm is developed using a novel simila...
In cluster analysis, data are clustered into meaningful groups so that the objects in the same group...
Clustering is a useful technique that organizes a large quantity of unordered datasets into a small ...
Efficient learning of a data analysis task strongly depends on the data representation. Most methods...
A method for determining the mutual nearest neighbours (MNN) and mutual neighbourhood value (mnv) of...
Clustering algorithms partition a collection of objects into a certain number of clusters (groups, s...
For using Data Mining, especially cluster analysis, one needs measures to determine the similarity o...
Many clustering methods partition the data groups based on the input data similarity matrix. Thus, t...
In this paper, a novel similarity measure for estimating the degree of similarity between two symbol...
In this paper, we introduce a novel similarity measure for relational data. It is the first measure ...
Similarity-based clustering is a simple but powerful technique which usually results in a clustering...
Clustering is an unsupervised learning technique which aims at grouping a set of objects into cluste...
In many algorithms in the field of data mining to perform clustering of given data, notion of ‘clust...
Pairwise clustering methods partition the data space into clusters by the pairwise similarity betwee...
Abstract The task of clustering is to identify classes of similar objects among a set of objects. Th...
A nonparametric, hierarchical, disaggregative clustering algorithm is developed using a novel simila...
In cluster analysis, data are clustered into meaningful groups so that the objects in the same group...
Clustering is a useful technique that organizes a large quantity of unordered datasets into a small ...
Efficient learning of a data analysis task strongly depends on the data representation. Most methods...
A method for determining the mutual nearest neighbours (MNN) and mutual neighbourhood value (mnv) of...
Clustering algorithms partition a collection of objects into a certain number of clusters (groups, s...
For using Data Mining, especially cluster analysis, one needs measures to determine the similarity o...
Many clustering methods partition the data groups based on the input data similarity matrix. Thus, t...
In this paper, a novel similarity measure for estimating the degree of similarity between two symbol...
In this paper, we introduce a novel similarity measure for relational data. It is the first measure ...
Similarity-based clustering is a simple but powerful technique which usually results in a clustering...